108 citations · 131 across the 10 of their papers we have counts for
10 papers
Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally
Manon Verbockhaven, Sylvain Chevallier, Guillaume Charpiat +1
Machine learning tasks are generally formulated as optimization problems, where one searches for an optimal function within a certain functional space. In practice, parameterized f…
Evaluating the structure of cognitive tasks with transfer learning
Bruno Aristimunha, Raphael Y. de Camargo, Walter H. Lopez Pinaya +3
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this chall…
End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities
Quentin Barthélemy, Sylvain Chevallier, Raphaëlle Bertrand-Lalo +1
In brain-computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus on the detection of P300, aiming for single trial classification for a spell…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Salim Khazem, Sylvain Chevallier, Quentin Barthélemy +2
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the…
Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Marie-Constance Corsi, Sylvain Chevallier, Fabrizio De Vico Fallani +1
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and…
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
Marie-Constance Corsi, Florian Yger, Sylvain Chevallier +1
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies…
Adaptive Visualisation System for Construction Building Information Models Using Saliency
Hugo Martin, Sylvain Chevallier, Eric Monacelli
Building Information Modeling (BIM) is a recent construction process based on a 3D model, containing every component related to the building achievement. Architects, structure engi…
Using Riemannian geometry for SSVEP-based Brain Computer Interface
Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals f…
Metrics for Multivariate Dictionaries
Sylvain Chevallier, Quentin Barthélemy, Jamal Atif
Overcomplete representations and dictionary learning algorithms kept attracting a growing interest in the machine learning community. This paper addresses the emerging problem of c…